Siamese-like trackers expose considerable shortcomings in the case of brief occlusion due mainly to the inadequate consideration of the correlation information between adjacent frames. The precision of predicted bounding boxes still has much room for further improvement because the traditional regression loss cannot effectively handle the case where one box contains the other. To address these shortages, the paper proposes a novel pre-frame auxiliary and fusion tracking framework. Within this framework, a retained variable is first introduced to avoid some additional twin branches while retaining the previously obtained deep features of the search frames. Based on such a variable, a pre-frame auxiliary module is constructed to establish the relationship between encoding features and the retained pre-frame information. Furthermore, a decoding fusion module is designed to fuse the generated similarity relationship between the template patch and the search patch and the one between the search frame and previous frames. Moreover, the Efficient IoU (EIoU) loss is employed to increase the precision of predicted bounding boxes by adding three penalty terms for the differences in the center point, length, and width of the two bounding boxes. Finally, the superiority over state-of-the-art methods is verified by numerous tests on visual tracking benchmarks.

类似连体的跟踪器在短暂遮挡的情况下暴露出相当大的缺点，这主要是由于没有充分考虑相邻帧之间的相关信息。预测边界框的精度仍有很大的进一步提高的空间，因为传统的回归损失不能有效地处理一个框包含另一个框的情况。为了解决这些不足，本文提出了一种新颖的前帧辅助和融合跟踪框架。在此框架内，首先引入保留变量以避免一些额外的孪生分支，同时保留先前获得的搜索框架的深层特征。基于这样的变量，构建前帧辅助模块来建立编码特征和保留的前帧信息之间的关系。此外，设计了解码融合模块来融合模板补丁和搜索补丁之间以及搜索帧和先前帧之间生成的相似关系。此外，通过针对两个边界框的中心点、长度和宽度的差异添加三个惩罚项，采用高效 IoU (EIoU) 损失来提高预测边界框的精度。最后，通过视觉跟踪基准的大量测试验证了相对于最先进方法的优越性。